HYDROLOGICAL PROCESSES Hydrol. Process. 20, 2669– 2692 (2006) Published online 11 May 2006 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hyp.6065
Estimating fog deposition at a Puerto Rican elfin cloud forest site: comparison of the water budget and eddy covariance methods F. Holwerda,1 * R. Burkard,2 W. Eugster,2,3 F. N. Scatena,4 A. G. C. A. Meesters1 and L. A. Bruijnzeel1 1
Faculty of Earth and Life Sciences, Vrije Universiteit, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands 2 Institute of Geography, University of Bern, Bern, Switzerland 3 Institute of Plant Sciences, ETH Zurich, Zurich, Switzerland 4 Department of Earth and Environmental Science, University of Pennsylvania, Philadelphia, PA 19104, USA
Abstract: The deposition of fog to a wind-exposed 3 m tall Puerto Rican cloud forest at 1010 m elevation was studied using the water budget and eddy covariance methods. Fog deposition was calculated from the water budget as throughfall plus stemflow plus interception loss minus rainfall corrected for wind-induced loss and effect of slope. The eddy covariance method was used to calculate the turbulent liquid cloud water flux from instantaneous turbulent deviations of the surface-normal wind component and cloud liquid water content as measured at 4 m above the forest canopy. Fog deposition rates according to the water budget under rain-free conditions (0Ð11 š 0Ð05 mm h#1 ) and rainy conditions (0Ð24 š 0Ð13 mm h#1 ) were about three to six times the eddy-covariance-based estimate (0Ð04 š 0Ð002 mm h#1 ). Under rain-free conditions, water-budget-based fog deposition rates were positively correlated with horizontal fluxes of liquid cloud water (as calculated from wind speed and liquid water content data). Under rainy conditions, the correlation became very poor, presumably because of errors in the corrected rainfall amounts and very high spatial variability in throughfall. It was demonstrated that the turbulent liquid cloud water fluxes as measured at 4 m above the forest could be only ¾40% of the fluxes at the canopy level itself due to condensation of moisture in air moving upslope. Other factors, which may have contributed to the discrepancy in results obtained with the two methods, were related to effects of footprint mismatch and methodological problems with rainfall measurements under the prevailing windy conditions. Best estimates of annual fog deposition amounted to ¾770 mm year#1 for the summit cloud forest just below the ridge top (according to the water budget method) and ¾785 mm year#1 for the cloud forest on the lower windward slope (using the eddy-covariance-based deposition rate corrected for estimated vertical flux divergence). Copyright 2006 John Wiley & Sons, Ltd. KEY WORDS
cloud forest; eddy covariance method; fog deposition; forest hydrology; interception; stemflow; throughfall; water budget
INTRODUCTION During the last two decades, the hydrological importance of tropical montane cloud forest (TMCF) has been recognized increasingly (Zadroga, 1981; Bruijnzeel, 2001). There is circumstantial evidence that a complete conversion of TMCF to pasture or vegetable cropping may have an adverse effect on total or seasonal water yield through the diminished capture of passing fog by the vegetation after clearing (Ingwersen, 1985; Brown et al., 1996; Bruijnzeel, 2001). Similar effects may be expected when the average cloud condensation level is raised because of regional warming of the atmosphere, be it by global climate change (Still et al., 1999; * Correspondence to: F. Holwerda, Department of Hydrology and Geo-Environmental Sciences, Faculty of Earth & Life Sciences, Vrije Universiteit, De Boeleaan, 1085– 1087, 1081 HV Amsterdam, The Netherlands. E-mail:
[email protected]
Copyright 2006 John Wiley & Sons, Ltd.
Received 5 May 2004 Accepted 12 May 2005
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Foster, 2001), or by large-scale clearing of forest at lower elevations (Lawton et al., 2001; Van der Molen, 2002). Although quantification of fog deposition in headwater areas with TMCF assumes particular importance in relation to the possible effects of cloud forest conversion or climate change on water yield, it is also notoriously difficult. One of the most commonly used approaches is simply to compare amounts of throughfall plus stemflow (net precipitation) with amounts of incident rainfall during periods with and without fog (Harr, 1982; Schellekens et al., 1998; Holder, 2003). Because losses via wet canopy evaporation are neglected, this technique provides a minimum estimate of fog deposition only (see Bruijnzeel (2001) for discussion). TMCF often occurs on wind-exposed ridges or mountain peaks, where rain tends to fall at an angle for much of the time. As a result, the effective depth of rainfall incident on sloping ground differs from that indicated by measurements made with a conventionally placed rain gauge (horizontal orifice; Sharon, 1980). Furthermore, rainfall measurements by gauges raised above ground level are subject to systematic error because of the distorted wind field above the gauge orifice, with the magnitude of the error depending mainly on rainfall intensity and wind speed (Nespor and Sevruk, 1999). As such, rainfall measurements at sites with TMCF are potentially subject to large uncertainty, which further complicates the quantification of fog water inputs via comparative measurements (cf. Hafkenscheid et al., 1998). During the past decade, the net turbulent exchange of liquid cloud water between the surface and the atmosphere has also been measured directly with the so-called eddy covariance method (Beswick et al., 1991; Vermeulen et al., 1997; Kowalski and Vong, 1999; Burkard et al., 2003). An advantage of this approach is that averaging over an area (the so-called footprint area) is implicit (Schuepp et al., 1990; Kowalski and Vong, 1999). The only study that has combined the eddy covariance method with hydrological measurements (rainfall, throughfall) indicated that eddy-covariance-based estimates of fog deposition (made above a coniferous forest on flat terrain in The Netherlands) were two to three times smaller than amounts of fog drip measured as throughfall (Vermeulen et al., 1997). This discrepancy was attributed to: (1) undetected contributions to throughfall by rainfall; (2) delayed release of rain and/or fog water stored on the canopy by wind action; and (3) uncertainty in the throughfall measurements (Vermeulen et al., 1997). Working in hilly terrain in the Pacific Northwest of the USA, Kowalski and Vong (1999) found that fluxes of liquid cloud water as measured at 10 and 15 m above a 6Ð8 m tall Douglas fir canopy diverged due to condensation processes within the ascending clouds. Consequently, actual rates of fog deposition at the canopy level were underestimated considerably (Kowalski and Vong, 1999). There is a need for additional comparative studies to test the applicability of the above-mentioned techniques. The latter is especially true for the type of topographically complex terrain in which many TMCFs are located. This paper presents the results of measurements of fog deposition rates to a low-statured Puerto Rican ridge top cloud forest made from 25 June through to 7 August 2002 using hydrological and micrometeorological techniques. The latter consisted of the direct measurement of the net turbulent flux of liquid cloud water with the same eddy covariance system as used previously by Burkard et al. (2003). Hydrological measurements included the determination of rainfall, throughfall, and stemflow. The rainfall measurements were corrected for wind-induced losses and for the effect of sloping ground. Interception loss was estimated using the wet variant of the Penman–Monteith equation. For each period for which amounts of throughfall and stemflow were measured, fog deposition was calculated as the difference between throughfall plus stemflow plus interception loss and corrected rainfall. Fog deposition rates estimated by the water budget approach and rates measured directly with the eddy covariance method are compared, and various possible reasons for the contrasting results are discussed.
STUDY AREA The study site was located at ¾1010 m elevation near Pico del Este (1052 m), the easternmost summit in the Luquillo Mountains, northeastern Puerto Rico (Figure 1). Measurements were made about 30 m below a Copyright 2006 John Wiley & Sons, Ltd.
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Figure 1. Location and topography of the experimental site near Pico del Este, Puerto Rico. Elevations are in metres, and contour line spacing is 10 m
ridge top on a 17° slope facing approximately N40E (i.e. almost perpendicular to the prevailing easterly to northeasterly trade winds; Brown et al., 1983). Average annual rainfall at Pico del Este is 4435 mm (Garc´ıaMartin´o et al., 1996). The mean monthly relative humidity below the forest canopy at Pico del Este is close to saturation (99Ð7%; Weaver, 1972). Cloud cover occurs about 60% of the time during the day and for nearly 100% of the time at night (Baynton, 1968). February and March have the lowest rainfall and least-frequent cloud cover (Asbury et al., 1994). Average monthly temperatures range between 17 and 20 ° C (Brown et al., 1983). The vegetation at Pico del Este is typical of the elfin cloud forest type found on peaks and ridges above 900 m in the Luquillo Mountains. The forest canopy consists of evergreen, broadleaf tree species, dominated by Tabebuia rigida, Ocotea spathulata, and Calyptranthes krugii with a mean height of ¾3 m (Weaver, 1995). The forest is rich in epiphytes, with mosses and bromeliads covering branches, stems, and part of the soil surface. Leaf area index (LAI, m2 m#2 ) as estimated from light extinction measurements (F. Holwerda, unpublished data) was 2Ð1 š 0Ð7, close to the value of 2Ð0 found by Weaver et al. (1986) for the same forest using destructive sampling methods. METHODS Meteorological parameters Above-canopy climatic measurements were made at a measuring interval of 10 s in a 7 m tall mast placed in a small, natural clearing. The measurements were recorded by a Campbell Scientific CR10X data logger, which stored average data every 10 min. Net radiation Rn !W m#2 " was measured with a Kipp & Zonen CNR1 net radiometer at a height of 5Ð3 m above the ground. Air temperature T (° C) and relative humidity RH (%) were measured with a Rotronic hygrothermometer (MP100A, equipped with radiation protection Copyright 2006 John Wiley & Sons, Ltd.
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shield) at 2 m. Wind speed U !m s#1 " and wind direction Udir (degrees) were measured at 6 m, using an A100R switching anemometer (Vector Instruments, UK) and a W200P potentiometer windvane (Vector Instruments, UK) respectively. To detect the presence or absence and the density of fog, a Present Weather Detector (PWD11, Vaisala, FI) was mounted at 5 m. This device evaluates the so-called meteorological optical range by measuring the intensity of infrared light scattered at an angle of 45° . The scatter measurements are converted to visibility (VIS, m) by analysis of the signal properties. Rainfall Rainfall P (mm) was measured with a tipping-bucket rain gauge (manufactured at the Vrije Universiteit, Amsterdam, 500 cm2 orifice, 0Ð10 mm per tip) and a totalizer rain gauge (100 cm2 orifice) serving as a backup. The recording gauge was placed in a small clearing about 10 m from the meteorological mast with the average angle between the gauge orifice (at 1Ð8 m above the ground) and the surrounding tree tops being ¾20° . The totalizer gauge was located at the ridge top, and the average angle to the vegetation near the gauge orifice (at 30 cm above the ground) was less than 20° . The recording gauge was connected to a Campbell Scientific 21X data logger and 5 min totals were stored in an external storage module. The totalizer gauge was usually read twice daily, both early in the morning and late in the afternoon, and occasionally once every 24 h. Correction of rainfall measurements Wind-induced loss. Underestimation of rainfall because of the distorted wind field above the gauge orifice can be an important source of error at windy sites. The wind-induced error is, on average, 2–10% of the rainfall, with the error depending mainly on the size of the raindrops and the wind speed at the height of the gauge orifice (Nespor and Sevruk, 1999). The recording rain-gauge measurements were corrected for wind-induced losses using (Førland et al., 1996) Rc D kR
!1"
where Rc !mm h#1 " is the corrected rainfall intensity, k is a correction factor for wind-induced loss, and R !mm h#1 " is the measured rainfall rate. For liquid precipitation, k depends solely on rainfall intensity R !mm h#1 " and the wind speed at gauge height Ug !m s#1 " according to k D exp[#0Ð0010 ln!R" # 0Ð0122Ug ln!R" C 0Ð034Ug C 0Ð0077]
!2"
The wind speed at gauge height Ug was estimated using (Sevruk and Zahlavova, 1994) Ug D
ln!zg /z0 " !1 # 0Ð024˛"U ln!zm /z0 "
!3"
in which zg is the height of the gauge orifice (1Ð8 m), z0 the roughness length (0Ð3 m, estimated as 10% of average canopy height; Garrat, 1992), U is the wind speed at measuring height zm (6 m), and ˛ is the average angle to the vegetation in the vicinity of the gauge (¾20° ). The correction factor k, and therefore Ug , were calculated for each rainfall event. Restrepo-Posada and Eagleson (1982) developed a methodology to separate time series of rainfall into statistically independent events. Using rainfall records from a wide range of climates, they also produced an empirical relation for estimating the minimum time between independent storms from the mean annual rainfall and the length of the wet season. For the Pico del Este data (mean annual rainfall of 4435 mm and 12-month wet season; Garc´ıa-Martin´o et al., 1996), the minimum separation was estimated to be in the order of 1–2 h. A 2 h dry period was used in this analysis. The sensitivity of the calculations below to different event definitions is Copyright 2006 John Wiley & Sons, Ltd.
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tested in the Discussion section. Ug was calculated from the average wind speed U for each event. The average rainfall intensity R !mm h#1 " during an event was calculated using n
RD
60 ! Pi d iD1
!4"
in which Pi (mm) is the amount of rainfall over a 5 min interval, n is the number of 5 min intervals during which rainfall was recorded, and d (min) is the total duration of the event. Effect of sloping ground. The intensity at which rain is intercepted by a given surface depends on the angle of incidence, i.e. it is highest when rain falls normal to the surface, and becomes lower when rain falls at an angle (Sharon, 1980). The tipping-bucket rain gauge was installed horizontally on a 17° slope that was roughly orientated perpendicularly to the northeasterly trade winds (Figure 1). Because of the windy conditions prevailing at Pico del Este, rain fell at a considerable angle for much of the time, and the actual rainfall incident on the forest canopy must, therefore, have been higher than that measured by the rain gauge. To correct for this effect, a trigonometric model was used which allows the computation of rainfall intensity normal to a slope from conventional (horizontal) rainfall measurements when the azimuth and angle of the inclined rainfall can be accurately specified (Sharon, 1980; Figure 2). For each rainfall event, the median rainfall inclination angle was calculated using relationships between rainfall intensity, raindrop size, and terminal fall velocity of the raindrops, and wind speed as outlined below. First, the median raindrop diameter D (mm) was calculated from the wind-corrected rainfall intensity Rc using (Laws and Parsons, 1943) !5" D D 2Ð23!0Ð039 37Rc "0Ð102 The terminal fall velocity VD !m s#1 " of the raindrops was then calculated from D using (Gunn and Kinzer, 1949) VD D 3Ð378 ln!D" C 4Ð213 !6" Next, the average rainfall inclination angle was calculated using (Sharon, 1980; Herwitz and Slye, 1995) tan!b" D
U VD
!7"
Figure 2. When rain falls at an angle b to a sloping surface with angle a, the intensity of rainfall intercepted by the sloping surface Ra0 differs from that measured by a horizontal rain gauge Rc . Pa0 is the rainfall depth along the normal to the slope and Pa is the depth along the normal to the horizontal. Adapted from Sharon (1980)
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where b is the angle of rainfall inclination in degrees from the vertical (Figure 2) and U is the average wind speed during an event (as measured at 6 m). Rainfall intensity along the normal to an inclined surface Ra0 !mm h#1 " relates to horizontally measured rainfall intensity Rc !mm h#1 " as follows (Sharon, 1980; Figure 2): Ra0 D [cos!a" C sin!a" tan!b" cos!#a # #b "] Rc
!8"
in which a is the inclination of the sloping ground (17° ), #a the slope aspect (40° , Figure 1), and #b the average direction of the wind, and therefore of the rain per event (degrees). Integrating rainfall intensity over time gives rainfall depth; therefore (Sharon, 1980): Pa0 D [cos!a" C sin!a" tan!b" cos!#a # #b "] Pc
!9"
where Pa0 is the rainfall depth along the normal to the sloping ground (Figure 2) and Pc is the rainfall total as measured with the tipping-bucket rain gauge and corrected for wind-induced loss. The vertical equivalent of Pa0 (Sharon, 1980) Pa0 !10" Pa D cos!a" needs to be used in Equation (9), so that (Sharon, 1980) fc D
Pa D 1 C tan!a" tan!b" cos!#a # #b " Pc
!11"
where fc is a correction factor applied to the tipping-bucket rain gauge measurements after correction for wind-induced loss Pc to yield the ‘true’ quantity of rain incident to the forest canopy Pa (Figure 2). In the above model, it is assumed that the canopy surface runs parallel to the sloping ground (Figure 2). Furthermore, it is assumed that the canopy intercepts all rainfall incident to it (Pa0 ), i.e. the proportion of rain reaching the ground without touching the canopy is assumed to be negligible. Throughfall and stemflow Throughfall TF (mm) was measured using 20 totalizer gauges, each with an orifice of 100 cm2 and placed horizontally at ¾30 cm above the ground. A roving sampling technique was used to minimize the effects of spatial variability (Lloyd and Marques, 1988). An 80 m transect was outlined with numbered flags placed at 1 m intervals, representing 80 potential sampling points. The gauges were positioned by randomly selecting 20 from the 80 possible sampling points. This procedure was repeated each time the gauges were emptied. Gauges were generally read twice daily, i.e. early in the morning and late in the afternoon, and occasionally once every 24 h. This resulted in 72 gauge relocations during the 44-day study period. In addition, two steel tipping-bucket TF gutters (projected surface areas of 1Ð08 and 0Ð98 m2 ) were used to obtain continuous records of canopy drip. The tipping-bucket systems were connected to a Campbell Scientific 21X data logger and 5 min totals were stored in an external storage module. For the estimation of stemflow SF (mm), two trees of average dimensions were selected within the transect. At approximately 0Ð5 m from the ground, a ¾30 cm section of the stem was cleaned of epiphytes and mosses, and silicone tubing slit open attached to the stem in a spiral fashion. Any remaining spaces between tubing and stem were sealed with silicone sealant. Containers (3Ð75 l capacity) were used to collect the stemflow. The average value of the two collected volumes per sampling period (litres/stem) was multiplied by the overall stem density of 2520 ha#1 (Weaver, 1999) to obtain an approximate areal estimate of SF (mm). The SF gauges were read every time the TF gauges were emptied. Copyright 2006 John Wiley & Sons, Ltd.
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Interception loss Interception loss was approximated using (Gash, 1979) Ei D
n ! jD1
Ew,j C mC
!12"
where Ei is the total interception loss (mm), n is the total number of half-hourly periods that the canopy was considered fully wet, Ew,j (mm) is the wet canopy evaporation over a half-hourly period j, C (mm) is the canopy saturation value, and m is the number of times that the canopy dried up completely. The occurrence of drip from the canopy, as recorded by the TF gutters, was used to define fully wetted canopy conditions. For those periods, wet canopy evaporation rates Ew were calculated using the wet variant of the Penman–Monteith equation (Monteith, 1965): A C &Cp VPD/ra $Ew D !13" C' in which $ !J kg#1 " is the latent heat of vaporization, !Pa K#1 " is the slope of the temperature–vapour pressure relationship at temperature T, A (W m#2 ) is the available energy, & !kg m#3 " is the density of air, Cp !J kg#1 K#1 " is the specific heat at constant pressure, VPD (Pa) is the vapour pressure deficit, ra !s m#1 " is the aerodynamic resistance, and ' !Pa K#1 " is the psychrometric constant. A is usually calculated as the difference between net radiation Rn and the soil heat flux G. G was not measured; so, to account for energy losses into the soil, Rn was reduced by 3% on the basis of previous measurements at Pico del Este (F Holwerda, unpublished data). VPD is normally calculated from temperature and RH data as the difference between saturated and actual vapour pressures. However, accurate measurements of RH under the prevailing very high humidity levels could not be obtained for most of the time. Therefore, a fixed value of 100% was assumed in the calculations of $Ew , thereby rendering the aerodynamic term zero in Equation (13). Although this choice may seem rather radical at first sight, it is considered justified because of the frequently foggy conditions at Pico del Este. For 96% of the time that the canopy was wet (as indicated by the TF gutters), visibility was
